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Ma S, Ge J, Qin L, Chen X, Du L, Qi Y, Bai L, Han Y, Xie Z, Chen J, Jia Y. Spatiotemporal Epidemiological Trends of Mpox in Mainland China: Spatiotemporal Ecological Comparison Study. JMIR Public Health Surveill 2024; 10:e57807. [PMID: 38896444 PMCID: PMC11229661 DOI: 10.2196/57807] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Revised: 04/08/2024] [Accepted: 04/29/2024] [Indexed: 06/21/2024] Open
Abstract
BACKGROUND The World Health Organization declared mpox an international public health emergency. Since January 1, 2022, China has been ranked among the top 10 countries most affected by the mpox outbreak globally. However, there is a lack of spatial epidemiological studies on mpox, which are crucial for accurately mapping the spatial distribution and clustering of the disease. OBJECTIVE This study aims to provide geographically accurate visual evidence to determine priority areas for mpox prevention and control. METHODS Locally confirmed mpox cases were collected between June and November 2023 from 31 provinces of mainland China excluding Taiwan, Macao, and Hong Kong. Spatiotemporal epidemiological analyses, including spatial autocorrelation and regression analyses, were conducted to identify the spatiotemporal characteristics and clustering patterns of mpox attack rate and its spatial relationship with sociodemographic and socioeconomic factors. RESULTS From June to November 2023, a total of 1610 locally confirmed mpox cases were reported in 30 provinces in mainland China, resulting in an attack rate of 11.40 per 10 million people. Global spatial autocorrelation analysis showed that in July (Moran I=0.0938; P=.08), August (Moran I=0.1276; P=.08), and September (Moran I=0.0934; P=.07), the attack rates of mpox exhibited a clustered pattern and positive spatial autocorrelation. The Getis-Ord Gi* statistics identified hot spots of mpox attack rates in Beijing, Tianjin, Shanghai, Jiangsu, and Hainan. Beijing and Tianjin were consistent hot spots from June to October. No cold spots with low mpox attack rates were detected by the Getis-Ord Gi* statistics. Local Moran I statistics identified a high-high (HH) clustering of mpox attack rates in Guangdong, Beijing, and Tianjin. Guangdong province consistently exhibited HH clustering from June to November, while Beijing and Tianjin were identified as HH clusters from July to September. Low-low clusters were mainly located in Inner Mongolia, Xinjiang, Xizang, Qinghai, and Gansu. Ordinary least squares regression models showed that the cumulative mpox attack rates were significantly and positively associated with the proportion of the urban population (t0.05/2,1=2.4041 P=.02), per capita gross domestic product (t0.05/2,1=2.6955; P=.01), per capita disposable income (t0.05/2,1=2.8303; P=.008), per capita consumption expenditure (PCCE; t0.05/2,1=2.7452; P=.01), and PCCE for health care (t0.05/2,1=2.5924; P=.01). The geographically weighted regression models indicated a positive association and spatial heterogeneity between cumulative mpox attack rates and the proportion of the urban population, per capita gross domestic product, per capita disposable income, and PCCE, with high R2 values in north and northeast China. CONCLUSIONS Hot spots and HH clustering of mpox attack rates identified by local spatial autocorrelation analysis should be considered key areas for precision prevention and control of mpox. Specifically, Guangdong, Beijing, and Tianjin provinces should be prioritized for mpox prevention and control. These findings provide geographically precise and visualized evidence to assist in identifying key areas for targeted prevention and control.
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Affiliation(s)
- Shuli Ma
- School of Public Health, Qiqihar Medical University, Qiqihar, China
| | - Jie Ge
- School of Public Health, Qiqihar Medical University, Qiqihar, China
| | - Lei Qin
- Scientific Research Office, Qiqihar Medical University, Qiqihar, China
| | - Xiaoting Chen
- Scientific Research Office, Qiqihar Medical University, Qiqihar, China
| | - Linlin Du
- School of Public Health, Qiqihar Medical University, Qiqihar, China
| | - Yanbo Qi
- School of Public Health, Qiqihar Medical University, Qiqihar, China
| | - Li Bai
- School of Public Health, Qiqihar Medical University, Qiqihar, China
| | - Yunfeng Han
- School of Public Health, Qiqihar Medical University, Qiqihar, China
| | - Zhiping Xie
- School of Public Health, Qiqihar Medical University, Qiqihar, China
| | - Jiaxin Chen
- School of Public Health, Qiqihar Medical University, Qiqihar, China
| | - Yuehui Jia
- School of Public Health, Qiqihar Medical University, Qiqihar, China
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Yan S, Liu G, Chen X. Spatiotemporal distribution characteristics and influencing factors of the rate of cardiovascular hospitalization in Ganzhou city of China. Front Cardiovasc Med 2023; 10:1225878. [PMID: 38188258 PMCID: PMC10770874 DOI: 10.3389/fcvm.2023.1225878] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2023] [Accepted: 12/04/2023] [Indexed: 01/09/2024] Open
Abstract
Aims The objective of this study was to analyze hospitalization rates for cardiovascular diseases (CVD) in Ganzhou City, Jiangxi Province of China from 2015 to 2020 and to uncover the spatiotemporal distribution characteristics and influencing factors, and thus to provide reference for the prevention and control of CVD and public health resources planning. Methods The hospitalization data for CVDs from 2016 to 2020 was obtained from the First Affiliated Hospital of Gannan Medical University, and ArcGIS 10.8, SaTScan 9.5, and Matlab 20.0 were used to analyze the spatial autocorrelation, spatiotemporal scan statistics, and potential affecting factors of the hospitalization rates. Results The hospitalization rate for CVDs in Ganzhou City showed a slightly increasing trend from 2016 to 2020, with higher rates in winter and summer than that in spring and autumn, and the individuals aged 61 and above constitute a higher proportion compared to other age groups. Additionally, there was a positive correlation between hospitalization rates for CVDs and the counties and districts in Ganzhou City, with high-high aggregation areas mainly distributed in Nankang District, the western urban area of Ganzhou City. The spatial scan analysis identified three different types of significant aggregation areas: high-risk, low-risk, and middle-risk areas. The high-risk area was mainly centered around Zhanggong District or Shangyu County in the central and western regions, with a disease hospitalization rate 2-3 times higher than the rest areas. The study also found that environmental meteorological factors such as the annual average concentration of NO2, O3, average annual temperature, and annual maximum temperature diurnal range had a significant positive effect on hospitalization rates for CVDs in Ganzhou City, with O3 concentration and average annual temperature having significant positive indirect spatial spillover effects. Conclusion Winter and summer are the seasons with high hospitalization rate of cardiovascular diseases. County residents aged 61 and above are the higher-risk population that needs to pay more attention on for prevention and control of CVD in Ganzhou City, which exhibits significant spatiotemporal clustering. The urban areas of Zhanggong and Nankang in Ganzhou City are the key areas for prevention and control of CVD. The hospitalization rate of CVD in Ganzhou City is influenced by the aforementioned four environmental meteorological factors, with the annual maximum temperature diurnal range showing the most significant positive direct effect.
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Affiliation(s)
- Shanshan Yan
- School of Public Health and Health Management, Gannan Medical University, Ganzhou, China
- Key Laboratory of Prevention and Treatment of Cardiovascular and Cerebrovascular Diseases, Ministry of Education, Gannan Medical University, Ganzhou, China
| | - Guoqiu Liu
- School of Public Health and Health Management, Gannan Medical University, Ganzhou, China
| | - Xiaoyuan Chen
- School of Public Health and Health Management, Gannan Medical University, Ganzhou, China
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Wang G, Yang FF, Lin G, Wang Z, Zhang X. Modification of low temperature-related hospital admissions for cardiovascular diseases by multiple green space indicators at multiple spatial scales: Evidence from Guangzhou, China. Int J Hyg Environ Health 2023; 251:114193. [PMID: 37247607 DOI: 10.1016/j.ijheh.2023.114193] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 05/21/2023] [Accepted: 05/24/2023] [Indexed: 05/31/2023]
Abstract
BACKGROUND Extreme temperatures have an adverse effect on the occurrence of cardiovascular diseases (CVDs). Previous literatures tend to discuss the modification of CVDs occurrence by green space under high temperature. Relatively less attention is paid to the modification under low temperature. The variation of different attributes and spatial scales of green space in affecting CVDs occurrence are also overlooked. METHODS This study collected a total of 4364 first-time admission cases due to CVDs in a tertiary hospital in Guangzhou from 2012 to 2018, measured the scale of green space by greening rate (GR) and percentage of landscape (PLAND), the distribution of green space by patch density (PD), mean nearest neighbor distance (ENN_MN) and largest patch index (LPI), and the accessibility of green space by green patch accessibility index (GPAI). Using the time stratified case crossover design method, the modification of low temperature-related CVDs occurrence by the above green space indicators is evaluated in an area with a radius of 100-1000 m which is further divided at an interval of 100 m. RESULTS We found high GR, high PLAND, high PD, low ENN_MN, high LPI, and low GPAI corresponds to low risk of CVDs occurrence, the optimal modification scale of each green space indicator, which is radius corresponding to the maximum risk difference between high and low indicator subgroups, is around 800 m (GR), 600 m (PLAND and PD), 500 m (GPAI), and 300 m (LPI and ENN_MN), respectively. As the temperature decreases further, the health benefit from low GPAI at the optimal scale is weakened, whereas the benefits from the others are strengthened. CONCLUSIONS Low temperature related CVDs occurrence risk can be modified by multiple green space indicators, and these modifications have spatial scale effect. Our findings have important theoretical and practical significance for the formulation and implementation of local green space policies.
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Affiliation(s)
- Guobin Wang
- School of Geography and Planning, Sun Yat-Sen University, GuangZhou, 510006, China
| | - Fiona Fan Yang
- School of Geography and Planning, Sun Yat-Sen University, GuangZhou, 510006, China
| | - Geng Lin
- School of Geography and Planning, Sun Yat-Sen University, GuangZhou, 510006, China.
| | - Zhuoqing Wang
- Department of Scientific Research & Discipline Development, The First Affiliated Hospital Sun Yat-sen University, 58 Zhongshan Road 2nd, Guangzhou, 510080, China.
| | - Xiangxue Zhang
- State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing, 100875, China
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Gou A, Tan G, Ding X, Wang J, Jiao Y, Gou C, Tan Q. Spatial association between green space and COPD mortality: a township-level ecological study in Chongqing, China. BMC Pulm Med 2023; 23:89. [PMID: 36932348 PMCID: PMC10024412 DOI: 10.1186/s12890-023-02359-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Accepted: 02/10/2023] [Indexed: 03/19/2023] Open
Abstract
BACKGROUND There are regional differences in the effect of green space on mortality of Chronic obstructive pulmonary disease (COPD). We conduct an ecological study, using the administrative divisions of Chongqing townships in China as the basic unit, to investigate the association between COPD mortality and green space based on data of 313,013 COPD deaths in Chongqing from 2012 to 2020. Green space is defined by Fractional vegetation cover (FVC), which is further calculated based on the normalised vegetation index (NDVI) from satellite remote sensing imagery maps. METHODS After processing the data, the non-linear relationship between green space and COPD mortality is revealed by generalised additive models; the spatial differences between green space and COPD mortality is described by geographically weighted regression models; and finally, the interpretive power and interaction of each factor on the spatial distribution of COPD mortality is examined by a geographic probe. RESULTS The results show that the FVC local regression coefficients ranged from - 0.0397 to 0.0478, 63.0% of the regions in Chongqing have a positive correlation between green space and COPD mortality while 37.0% of the regions mainly in the northeast and west have a negative correlation. The interpretive power of the FVC factor on the spatial distribution of COPD mortality is 0.08. CONCLUSIONS Green space may be a potential risk factor for increased COPD mortality in some regions of Chongqing. This study is the first to reveal the relationship between COPD mortality and green space in Chongqing at the township scale, providing a basis for public health policy formulation in Chongqing.
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Affiliation(s)
- Aiping Gou
- College of Ecological Technology and Engineering, Shanghai Institute of Technology, Shanghai, 201418, China
| | - Guanzheng Tan
- College of Ecological Technology and Engineering, Shanghai Institute of Technology, Shanghai, 201418, China
| | - Xianbin Ding
- Institute of Chronic and Non-communicable Disease Control and Prevention, Chongqing Center for Disease Control and Prevention, Chongqing, 400042, China.
| | - Jiangbo Wang
- College of Architecture, Nanjing Tech University, Nanjing, 211816, China.
| | - Yan Jiao
- Institute of Chronic and Non-communicable Disease Control and Prevention, Chongqing Center for Disease Control and Prevention, Chongqing, 400042, China
| | - Chunyan Gou
- Department of Acupuncture, Chongqing Traditional Chinese Medicine Hospital, Chongqing, 400021, China
| | - Qiang Tan
- Institute of Chronic and Non-communicable Disease Control and Prevention, Chongqing Center for Disease Control and Prevention, Chongqing, 400042, China
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Gohari A, Gohari A, Ahmad AB. Importance of green roof criteria for residential and governmental buildings: a multi-criteria decision analysis. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:3707-3725. [PMID: 35953748 DOI: 10.1007/s11356-022-22472-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Accepted: 08/06/2022] [Indexed: 06/15/2023]
Abstract
Megacities recently are experiencing a shortage of green spaces basically due to the rapid growth of urbanization and increasing demand for different building types. Consideration of sustainable urban development is essential since the expansion of city facilities should be in line with social, economic, and environmental aspects. In this regard, green roof technology has been recommended as an effective solution for the growth of green spaces per capita and improving sustainability means of urban developments due to its diverse advantages. This study thus aimed at prioritizing sustainability indicators and relative sub-criteria of adopting green roof technology for residential and governmental buildings in the city of Mashhad, Iran, which has a dry climate. For this purpose, thirteen sub-criteria, which are extracted from the existing literature, are classified into three main sustainability indicators (environmental, economic, and social). Also, the best-worth method (BWM) as a multi-criteria decision-making technique was implemented to prioritize indicators and sub-criteria by analyzing the expert's opinion. The results indicated that respective economic and environmental indicators attract the highest priority in residential and governmental buildings. Additionally, the most important sub-criteria in environmental, economic, and social groups are air quality, roof longevity, and public health in both building types, respectively. However, when all criteria were considered, the respective highest priorities belong to roof longevity and air quality in residential and governmental buildings, while biodiversity conservation is the least important one in both building types. The results of this research can be beneficial in other cities with similar economic and climate conditions.
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Affiliation(s)
- Ali Gohari
- Department of Civil Engineering, Architecture and Urbanism, Sadjad University of Technology, No. 64 Jalal Al Ahmad St., Mashhad, 91881-48848, Iran
| | - Adel Gohari
- Department of Geoinformation, Faculty of Built Environment and Surveying, Universiti Teknologi Malaysia, Skudai, 81310, Johor, Malaysia.
| | - Anuar Bin Ahmad
- Department of Geoinformation, Faculty of Built Environment and Surveying, Universiti Teknologi Malaysia, Skudai, 81310, Johor, Malaysia
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Vallarta-Robledo JR, Joost S, Vieira Ruas MA, Gubelmann C, Vollenweider P, Marques-Vidal P, Guessous I. Geographic clusters of objectively measured physical activity and the characteristics of their built environment in a Swiss urban area. PLoS One 2022; 17:e0252255. [PMID: 35196322 PMCID: PMC8865698 DOI: 10.1371/journal.pone.0252255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Accepted: 01/26/2022] [Indexed: 11/29/2022] Open
Abstract
Introduction Evidence suggests that the built environment can influence the intensity of physical activity. However, despite the importance of the geographic context, most of the studies do not consider the spatial framework of this association. We aimed to assess individual spatial dependence of objectively measured moderate and vigorous physical activity (MVPA) and describe the characteristics of the built environment among spatial clusters of MVPA. Methods Cross-sectional data from the second follow-up (2014–2017) of CoLaus|PsyCoLaus, a longitudinal population-based study of the Lausanne area (Switzerland), was used to objectively measure MVPA using accelerometers. Local Moran’s I was used to assess the spatial dependence of MVPA and detect geographic clusters of low and high MVPA. Additionally, the characteristics of the built environment observed in the clusters based on raw MVPA and MVPA adjusted for socioeconomic and demographic factors were compared. Results Data from 1,889 participants (median age 63, 55% women) were used. The geographic distribution of MVPA and the characteristics of the built environment among clusters were similar for raw and adjusted MVPA. In the adjusted model, we found a low concentration of individuals within spatial clusters of high MVPA (median: 38.5mins; 3% of the studied population) and low MVPA (median: 10.9 mins; 2% of the studied population). Yet, clear differences were found in both models between clusters regarding the built environment; high MVPA clusters were located in areas where specific compositions of the built environment favor physical activity. Conclusions Our results suggest the built environment may influence local spatial patterns of MVPA independently of socioeconomic and demographic factors. Interventions in the built environment should be considered to promote physically active behaviors in urban areas.
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Affiliation(s)
- Juan R Vallarta-Robledo
- Division and Department of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
- Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Group of Geographic Information Research and Analysis in Population Health (GIRAPH), Geneva, Switzerland
| | - Stéphane Joost
- Division and Department of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
- Group of Geographic Information Research and Analysis in Population Health (GIRAPH), Geneva, Switzerland
- Laboratory of Geographic Information Systems (LASIG), School of Architecture, Civil and Environmental Engineering (ENAC), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- La Source, School of Nursing, University of Applied Sciences and Arts Western Switzerland (HES-SO), Lausanne, Switzerland
| | - Marco André Vieira Ruas
- Laboratory of Geographic Information Systems (LASIG), School of Architecture, Civil and Environmental Engineering (ENAC), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Cédric Gubelmann
- Department of Medicine, Internal Medicine, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
| | - Peter Vollenweider
- Department of Medicine, Internal Medicine, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
| | - Pedro Marques-Vidal
- Group of Geographic Information Research and Analysis in Population Health (GIRAPH), Geneva, Switzerland
- Department of Medicine, Internal Medicine, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
| | - Idris Guessous
- Division and Department of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
- Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Group of Geographic Information Research and Analysis in Population Health (GIRAPH), Geneva, Switzerland
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